3888_covid-A_6_R_Ken
Ken Mao
2022-05-16
1 Question
Does a higher ICU admission and hospitalization predict a higher mortality rate in developing countries like Malaysia, and Chile compared to developed countries like the United States, and Switzerland?
2 Data setup
2.1 Library
library(tidyverse)
library(readxl)
library(tibble)
library(janitor)
library(reshape2)
library(ggplot2)
library(ggthemes)
library(plotly)
library(pheatmap)
library(maps)
library(zoo)
library(proxy)
library(DT)
library(limma)
library(RSpectra)
library(dplyr)
library(lubridate)
library(GGally)
# library(randomcoloR)
library(forecast)
# For url
library(XML)
library(RCurl)
library(rlist)2.2 About data
For this lab, we will examine the global collection of COVID-19
statistics maintained by Our World in Data. The information is updated
daily and includes data on confirmed cases, deaths, number of
administered vaccination and many others. To find out more details about
this data resources, please check out the detailed overview described on
their website and GitHub repository https://github.com/owid/covid-19-data/tree/master/public/data/.
The aim is to study the collective time-varying trends in number of
cases and infectivity of COVID-19. We approach this problem by first
studying the 25 most severely impacted countries by way of total case
numbers. These are: United States, India, Brazil, France, UK, Russia,
Turkey, Italy, Germany, Spain, Argentina, Iran, Colombia, Poland,
Mexico, Netherlands, Indonesia, Ukraine, South Africa, Philippines,
Peru, Belgium, Czechia, Japan, Israel.
在本实验室中，我们将检查由 Our World in Data 维护的 COVID-19
统计数据的全球集合。该信息每天更新，包括确诊病例、死亡人数、接种疫苗次数等数据。要了解有关此数据资源的更多详细信息，请查看其网站和
GitHub 存储库 https://github.com/owid/covid-19-data/tree/master/public/data/
上描述的详细概述。目的是研究 COVID-19
病例数和传染性的集体随时间变化趋势。我们首先通过总病例数研究 25
个受影响最严重的国家来解决这个问题。它们是：美国、印度、巴西、法国、英国、俄罗斯、土耳其、意大利、德国、西班牙、阿根廷、伊朗、哥伦比亚、波兰、墨西哥、荷兰、印度尼西亚、乌克兰、南非、菲律宾、秘鲁、比利时、捷克、日本、以色列。
United States, Singapore, Philippines, Switzerland.
2.3 Data input and extraction
We encourage you to extract data directly from the web and repeat the
lab. If your computer lab is fully reproducible, you will find that you
do not need to change your code when you update your result. The
pre-downloaded data from 16th February 2022 is given to you as
“owid-covid-data-160222.csv”.
我们鼓励您直接从网络中提取数据并重复该实验。如果您的计算机实验室是完全可重现的，您会发现在更新结果时不需要更改代码。
2022 年 2 月 16
日的预下载数据以”owid-covid-data-160222.csv”的形式提供给您。
[a] The data in “owid-covid-data-160222.csv” contains a lot of
COVID-19 related information for each country, such as new cases, new
deaths, icu patients, new tests etc, as well as the demography
statistics for each country. Read in the data for the selected 25
countries and extract the data between 2020-06-01 and 2021-12-30. Name
your R data object as covid. Check to see the column date is coded as
the class Date and extract data related selected counties. Consider ways
to double check that you have extract the data correctly.
“owid-covid-data-160222.csv”中的数据包含每个国家的大量 COVID-19
相关信息，例如新病例、新死亡、ICU
患者、新测试等，以及人口统计每个国家的统计数据。读取选定的 25
个国家/地区的数据，提取 2020-06-01 和 2021-12-30 之间的数据。将您的 R
数据对象命名为
covid。检查以查看列日期被编码为类日期并提取与所选县相关的数据。考虑仔细检查您是否正确提取数据的方法。
# 所有地区icu的新增数据的计算（基于每日的统计量）
new_icu_covid_function <- function(location_icu_full) {
new_icu_list <- c()
is_first_time = TRUE
for (i in 1:length(location_icu_full$icu_covid)) {
if (is_first_time){
new_icu = location_icu_full$icu_covid[i]
is_first_time = FALSE
}
else {
new_icu = location_icu_full$icu_covid[i] - location_icu_full$icu_covid[i-1]
}
new_icu_list <- append(new_icu_list, new_icu)
}
location_icu_full <- cbind(location_icu_full, new_icu_covid = new_icu_list)
return(location_icu_full)
}# Read in dataset 所有数据导入工作
# covid主数据
# covid <- read.csv("data/owid-covid-data-160222.csv")
covid <- read.csv("https://github.com/owid/covid-19-data/raw/master/public/data/owid-covid-data.csv")
covid_used <- covid[c("location",
"date",
"new_cases_smoothed",
"new_cases_smoothed_per_million",
"new_deaths_smoothed",
"new_deaths_smoothed_per_million",
"population",
"population_density",
"human_development_index")]
# ---
# icu数据 - Chile
# chile_icu_patient <- read.csv("https://github.com/MinCiencia/Datos-COVID19/raw/master/output/producto8/UCI_T.csv")
chile_icu_full <- read.csv("https://github.com/MinCiencia/Datos-COVID19/raw/master/output/producto20/NumeroVentiladores_T.csv")
# chile_icu_patient <- rename(chile_icu_patient, date = Region)
# chile_icu_patient <- chile_icu_patient[-1:-2,]
# chile_icu_patient <- cbind(rowSums(chile_icu_patient[,-1]), chile_icu_patient)
# chile_icu_patient <- rename(chile_icu_patient, icu_patients = `rowSums(chile_icu_patient[, -1])`)
# chile_icu_patient <- chile_icu_patient[1:2]
chile_icu_full <- rename(chile_icu_full, c(date = Ventiladores, beds_icu = total, icu_covid = disponibles, icu_free = ocupados))
# chile_icu_full <- merge(chile_icu_patient, chile_icu_bed, by = "date")
chile_icu_full <- cbind(location = "Chile", chile_icu_full)
# rm("chile_icu_patient", "chile_icu_bed")
chile_icu_full <- rename(chile_icu_full, icu_total = beds_icu)
chile_icu_full <- cbind(chile_icu_full, icu_occupancy_rate = chile_icu_full$icu_covid / chile_icu_full$icu_total * 100)
chile_icu_full <- new_icu_covid_function(chile_icu_full)
# chile_icu_full <- cbind(chile_icu_full, new_icu_covid = chile_new_icu)
# rm(is_first_time)
# rm(chile_new_icu)
# ---
# icu数据 - Malaysia
# malaysia_icu_full <- read.csv("data/icu.csv")
malaysia_icu_full <- read.csv("https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/icu.csv")
# https://github.com/MoH-Malaysia/covid19-public/tree/main/epidemic#icu
malaysia_icu_full <- malaysia_icu_full[, -2]
malaysia_icu_full <- aggregate(cbind(beds_icu_total, icu_covid) ~ date, data = malaysia_icu_full, sum)
malaysia_icu_full <- cbind(location = "Malaysia", malaysia_icu_full)
malaysia_icu_full <- rename(malaysia_icu_full, icu_total = beds_icu_total)
malaysia_icu_full <- cbind(malaysia_icu_full, icu_free = malaysia_icu_full$icu_total - malaysia_icu_full$icu_covid)
malaysia_icu_full <- cbind(malaysia_icu_full, icu_occupancy_rate = malaysia_icu_full$icu_covid / malaysia_icu_full$icu_total * 100)
malaysia_icu_full <- new_icu_covid_function(malaysia_icu_full)
# ---
# icu数据 - United_states
# united_states_icu_full <- read.csv("data/COVID-19_Reported_Patient_Impact_and_Hospital_Capacity_by_State_Timeseries.csv")
united_states_icu_full <- read.csv("https://healthdata.gov/api/views/g62h-syeh/rows.csv?accessType=DOWNLOAD")
united_states_icu_full <- united_states_icu_full[, -1]
united_states_icu_full <- aggregate(cbind(total_staffed_adult_icu_beds, staffed_icu_adult_patients_confirmed_covid) ~ date, data = united_states_icu_full, sum)
united_states_icu_full <- cbind(location = "United States", united_states_icu_full)
united_states_icu_full <- rename(united_states_icu_full, icu_total = total_staffed_adult_icu_beds)
united_states_icu_full <- rename(united_states_icu_full, icu_covid = staffed_icu_adult_patients_confirmed_covid)
united_states_icu_full <- cbind(united_states_icu_full, icu_free = united_states_icu_full$icu_total - united_states_icu_full$icu_covid)
united_states_icu_full <- cbind(united_states_icu_full, icu_occupancy_rate = united_states_icu_full$icu_covid / united_states_icu_full$icu_total * 100)
united_states_icu_full <- new_icu_covid_function(united_states_icu_full)
# ---
# icu数据 - Switzerland
# switzerland_icu_full <- read.csv("data/bag_covid_19_data_csv_03_May_2022/COVID19HospCapacity_geoRegion.csv")
temp <- tempfile()
temp2 <- tempfile()
download.file("https://www.covid19.admin.ch/api/data/20220503-ls6se5v3/downloads/sources-csv.zip", temp)
unzip(zipfile = temp, exdir = temp2)
switzerland_icu_full <- read.csv(file.path(temp2, "data/COVID19HospCapacity_geoRegion.csv"))
rm("temp", "temp2")
switzerland_icu_full <- switzerland_icu_full[, -2]
switzerland_icu_full <- aggregate(cbind(ICU_Capacity, ICU_Covid19Patients, ICU_FreeCapacity) ~ date, data = switzerland_icu_full, sum)
switzerland_icu_full <- cbind(location = "Switzerland", switzerland_icu_full)
switzerland_icu_full <- rename(switzerland_icu_full, icu_total = ICU_Capacity)
switzerland_icu_full <- rename(switzerland_icu_full, icu_covid = ICU_Covid19Patients)
switzerland_icu_full <- rename(switzerland_icu_full, icu_free = ICU_FreeCapacity)
switzerland_icu_full <- cbind(switzerland_icu_full, icu_occupancy_rate = switzerland_icu_full$icu_covid / switzerland_icu_full$icu_total * 100)
switzerland_icu_full <- new_icu_covid_function(switzerland_icu_full)
# ---
# ICU data
# current_covid_patients_icu <- read.csv("https://github.com/owid/covid-19-data/raw/master/public/data/hospitalizations/covid-hospitalizations.csv")
# current_covid_patients_icu <- read.csv("data/current-covid-patients-icu.csv")
# ---
# Developed country data
# WAY1
developed_countries_full <- filter(covid_used, human_development_index >= 0.854)
developed_countries <- unique(developed_countries_full$location)
covid_used$developed <- ifelse(
covid_used$location %in% developed_countries,
"Yes",
"No")
# developing_countries <- test %>% #Filtering countries which are developing and already developed by their hdi(human development index)
# filter(hdi < 0.854)
# developing_countries <- subset(developing_countries, location!="World")
# WAY2
# tables <- getURL("https://worldpopulationreview.com/country-rankings/developed-countries",.opts = list(ssl.verifypeer = FALSE)) %>% readHTMLTable()
# developed_countries_full <- tables[[1]]
# rm(tables)
# developed_countries_full <- read.csv("data/csvData.csv")# Range of date
start_date <- "2020-06-01"
end_date <- "2021-12-31"
range_data <- function(data, start_date, end_date){
data$date <- as.Date(data$date)
data <- data[(data$date >= start_date & data$date <= end_date), ]
return(data)
}
covid_used <- range_data(covid_used, start_date, end_date)
malaysia_icu_full <- range_data(malaysia_icu_full, start_date, end_date)
chile_icu_full <- range_data(chile_icu_full, start_date, end_date)
united_states_icu_full <- range_data(united_states_icu_full, start_date, end_date)
switzerland_icu_full <- range_data(switzerland_icu_full, start_date, end_date)# List of countries to study
# countries <- c("United States",
# "India",
# "Brazil",
# "France",
# "United Kingdom",
# "Russia",
# "Turkey",
# "Italy",
# "Germany",
# "Spain",
# "Argentina",
# "Iran",
# "Colombia",
# "Poland",
# 'Mexico',
# "Netherlands",
# "Indonesia",
# "Ukraine",
# "South Africa",
# "Philippines",
# "Peru",
# "Belgium",
# "Czechia",
# "Japan",
# "Israel")
# Q1中选择的几个问题
countries <- c("Chile",
"Malaysia",
"Switzerland",
"United States")
countries <- sort(countries)
## selecting countries and required time period.
covid_used_selected <- covid_used[covid_used$location %in% countries, ]
## covid <- covid_full %>% filter(covid_full$location %in% countries) ## Alternative
covid_A6_k <- rbind(chile_icu_full, malaysia_icu_full, switzerland_icu_full, united_states_icu_full)
covid_A6_k <- merge(covid_used_selected, covid_A6_k, by = c("location", "date"))
covid_A6_k <- cbind(covid_A6_k, icu_total_per_million = covid_A6_k$icu_total / covid_A6_k$population * 1000000)
covid_A6_k <- cbind(covid_A6_k, icu_covid_per_million = covid_A6_k$icu_covid / covid_A6_k$population * 1000000)
covid_A6_k <- cbind(covid_A6_k, icu_free_per_million = covid_A6_k$icu_free / covid_A6_k$population * 1000000)
covid_A6_k <- cbind(covid_A6_k, new_icu_covid_per_million = covid_A6_k$new_icu_covid / covid_A6_k$population * 1000000)
covid_A6_k <- covid_A6_k %>% relocate(any_of(c(
"location",
"date",
"developed",
"new_cases_smoothed",
"new_cases_smoothed_per_million",
"new_deaths_smoothed",
"new_deaths_smoothed_per_million",
"new_icu_covid",
"new_icu_covid_per_million",
"icu_total",
"icu_total_per_million",
"icu_covid",
"icu_covid_per_million",
"icu_free",
"icu_free_per_million",
"icu_occupancy_rate",
"population",
"population_density",
"human_development_index"
)))
# Don't forget to check whether you have read your data in correctly.
# table(as.character(covid_full$location)) covid_A6_k_develop_number <- aggregate(
list(new_cases_smoothed = covid_A6_k$new_cases_smoothed,
new_deaths_smoothed = covid_A6_k$new_deaths_smoothed,
new_icu_covid = covid_A6_k$new_icu_covid,
icu_total = covid_A6_k$icu_total,
icu_covid = covid_A6_k$icu_covid,
icu_free = covid_A6_k$icu_free,
population = covid_A6_k$population),
by = list(date = covid_A6_k$date,
developed = covid_A6_k$developed),
sum)
covid_A6_k_develop_rate <- aggregate(
list(new_cases_smoothed_per_million = covid_A6_k$new_cases_smoothed_per_million,
new_deaths_smoothed_per_million = covid_A6_k$new_deaths_smoothed_per_million,
new_icu_covid_per_million = covid_A6_k$new_icu_covid_per_million,
icu_total_per_million = covid_A6_k$icu_total_per_million,
icu_covid_per_million = covid_A6_k$icu_covid_per_million,
icu_free_per_million = covid_A6_k$icu_free_per_million,
icu_occupancy_rate = covid_A6_k$icu_occupancy_rate,
population_density = covid_A6_k$population_density),
by = list(date = covid_A6_k$date,
developed = covid_A6_k$developed),
mean)
covid_A6_k_develop <- merge(covid_A6_k_develop_number, covid_A6_k_develop_rate, by = c("date", "developed"))
covid_A6_k_develop <- covid_A6_k_develop %>% relocate(any_of(c(
"date",
"developed",
"new_cases_smoothed",
"new_cases_smoothed_per_million",
"new_deaths_smoothed",
"new_deaths_smoothed_per_million",
"new_icu_covid",
"new_icu_covid_per_million",
"icu_total",
"icu_total_per_million",
"icu_covid",
"icu_covid_per_million",
"icu_free",
"icu_free_per_million",
"icu_occupancy_rate",
"population",
"population_density"
)))
rm("covid_A6_k_develop_number", "covid_A6_k_develop_rate")2.4 Output data.csv
write.csv(covid_A6_k,"data/processed/covid_A6_ken.csv", row.names = FALSE)
write.csv(covid_A6_k_develop,"data/processed/covid_A6_ken_develop.csv", row.names = FALSE)2.5 Initial data analysis
plot_new_cases_per_million <- ggplot(covid_A6_k,
aes(x = date,
y = new_cases_smoothed_per_million,
group = developed,
color = location)
) +
geom_line() +
ylab("Number of new cases") +
ggtitle("plot_new_cases_per_million") +
labs(color = "Country / Region")
plot_new_deaths_per_million <- ggplot(covid_A6_k,
aes(x = date,
y = new_deaths_smoothed_per_million,
group = developed,
color = location)
) +
geom_line() +
ylab("Number of new deaths") +
ggtitle("plot_new_deaths_per_million") +
labs(color = "Country / Region")
plot_new_icu_covid_per_million <- ggplot(covid_A6_k,
aes(x = date,
y = new_icu_covid_per_million,
group = developed,
color = location)
) +
geom_line() +
ylab("Number of new covid ICU") +
ggtitle("plot_new_icu_covid_per_million") +
labs(color = "Country / Region")
plot_icu_occupancy_rate <- ggplot(covid_A6_k,
aes(x = date,
y = icu_occupancy_rate,
group = developed,
color = location)
) +
geom_line() +
ylab("Number of ICU occupancy rate") +
ggtitle("plot_icu_occupancy_rate") +
labs(color = "Country / Region")
# par(mfrow=c(1,3))
#
# plot(plot_new_cases_smoothed_per_million)
# plot(plot_weekly_icu_admissions_per_million)
# plot(plot_new_deaths_smoothed_per_million)
ggplotly(plot_new_cases_per_million)ggplotly(plot_new_deaths_per_million)ggplotly(plot_new_icu_covid_per_million)ggplotly(plot_icu_occupancy_rate)plot_develop_new_cases_per_million <- ggplot(covid_A6_k_develop,
aes(x = date,
y = new_cases_smoothed_per_million,
group = developed,
color = developed)
) +
geom_line() +
ylab("Number of new cases") +
ggtitle("plot_new_cases_per_million") +
labs(color = "Developed Country / Region")
plot_develop_new_deaths_per_million <- ggplot(covid_A6_k_develop,
aes(x = date,
y = new_deaths_smoothed_per_million,
group = developed,
color = developed)
) +
geom_line() +
ylab("Number of new deaths") +
ggtitle("plot_new_deaths_per_million") +
labs(color = "Developed Country / Region")
plot_develop_new_icu_covid_per_million <- ggplot(covid_A6_k_develop,
aes(x = date,
y = new_icu_covid_per_million,
group = developed,
color = developed)
) +
geom_line() +
ylab("Number of new covid ICU") +
ggtitle("plot_new_icu_covid_per_million") +
labs(color = "Developed Country / Region")
plot_develop_icu_occupancy_rate <- ggplot(covid_A6_k_develop,
aes(x = date,
y = icu_occupancy_rate,
group = developed,
color = developed)
) +
geom_line() +
ylab("Number of ICU occupancy rate") +
ggtitle("plot_icu_occupancy_rate") +
labs(color = "Developed Country / Region")
ggplotly(plot_develop_new_cases_per_million)ggplotly(plot_develop_new_deaths_per_million)ggplotly(plot_develop_new_icu_covid_per_million)ggplotly(plot_develop_icu_occupancy_rate)The figure above highlights the evolution of the reproduction rate
for each country. Notably, there doesn’t appear to be a great deal of
consistency between various countries’ behaviours. The amplitude,
periodicity and general trends appears to display no global consistency.
If you wish to isolate a single country, double click on a single
country on the plotly and you can see it in an isolated setting.
上图突出了每个国家繁殖率的演变。
值得注意的是，各国的行为之间似乎没有很大的一致性。
幅度、周期性和总体趋势似乎没有显示出全局一致性。
如果你想孤立一个国家，在情节上双击一个国家，你可以在一个孤立的环境中看到它。
3 Verify
Functions
# logistic regression in new cases and icu and death in different location
covid_location_covid_A6_k <- function(covid, location_set) {
if (location_set == "Yes" | location_set == "No") {
covid_location <- filter(covid, developed == location_set)
covid_location <- covid_location[c("developed",
"date",
"new_cases_smoothed_per_million",
"new_deaths_smoothed_per_million",
"new_icu_covid_per_million")]
return(covid_location)
}
else {
covid_location <- filter(covid, location == location_set)
covid_location <- covid_location[c("location",
"date",
"new_cases_smoothed_per_million",
"new_deaths_smoothed_per_million",
"new_icu_covid_per_million")]
return(covid_location)
}
}
logi_cases_deaths_covid_A6_k <- function(covid, logi_location) {
covid_location <- covid_location_covid_A6_k(covid, logi_location)
logi_covid_location <- glm(formula = covid_location$new_cases_smoothed_per_million ~ covid_location$new_deaths_smoothed_per_million)
return(summary(logi_covid_location))
}
logi_deaths_cases_covid_A6_k <- function(covid, logi_location) {
covid_location <- covid_location_covid_A6_k(covid, logi_location)
logi_covid_location <- glm(formula = covid_location$new_deaths_smoothed_per_million ~ covid_location$new_cases_smoothed_per_million)
return(summary(logi_covid_location))
}
logi_cases_icu_covid_A6_k <- function(covid, logi_location) {
covid_location <- covid_location_covid_A6_k(covid, logi_location)
logi_covid_location <- glm(formula = covid_location$new_cases_smoothed_per_million ~ covid_location$new_icu_covid_per_million)
return(summary(logi_covid_location))
}
logi_icu_cases_covid_A6_k <- function(covid, logi_location) {
covid_location <- covid_location_covid_A6_k(covid, logi_location)
logi_covid_location <- glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_cases_smoothed_per_million)
return(summary(logi_covid_location))
}
logi_deaths_icu_covid_A6_k <- function(covid, logi_location) {
covid_location <- covid_location_covid_A6_k(covid, logi_location)
logi_covid_location <- glm(formula = covid_location$new_deaths_smoothed_per_million ~ covid_location$new_icu_covid_per_million)
return(summary(logi_covid_location))
}
logi_icu_deaths_covid_A6_k <- function(covid, logi_location) {
covid_location <- covid_location_covid_A6_k(covid, logi_location)
logi_covid_location <- glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_deaths_smoothed_per_million)
return(summary(logi_covid_location))
}3.1 Logistic regression
3.1.1 By Countries
logi_cases_deaths_chile <- logi_cases_deaths_covid_A6_k(covid_A6_k, "Chile")
logi_cases_deaths_malaysia <- logi_cases_deaths_covid_A6_k(covid_A6_k, "Malaysia")
logi_cases_deaths_switzerland <- logi_cases_deaths_covid_A6_k(covid_A6_k, "Switzerland")
logi_cases_deaths_united_states <- logi_cases_deaths_covid_A6_k(covid_A6_k, "United States")
logi_deaths_cases_chile <- logi_deaths_cases_covid_A6_k(covid_A6_k, "Chile")
logi_deaths_cases_malaysia <- logi_deaths_cases_covid_A6_k(covid_A6_k, "Malaysia")
logi_deaths_cases_switzerland <- logi_deaths_cases_covid_A6_k(covid_A6_k, "Switzerland")
logi_deaths_cases_united_states <- logi_deaths_cases_covid_A6_k(covid_A6_k, "United States")
logi_cases_icu_chile <- logi_cases_icu_covid_A6_k(covid_A6_k, "Chile")
logi_cases_icu_malaysia <- logi_cases_icu_covid_A6_k(covid_A6_k, "Malaysia")
logi_cases_icu_switzerland <- logi_cases_icu_covid_A6_k(covid_A6_k, "Switzerland")
logi_cases_icu_united_states <- logi_cases_icu_covid_A6_k(covid_A6_k, "United States")
logi_icu_cases_chile <- logi_icu_cases_covid_A6_k(covid_A6_k, "Chile")
logi_icu_cases_malaysia <- logi_icu_cases_covid_A6_k(covid_A6_k, "Malaysia")
logi_icu_cases_switzerland <- logi_icu_cases_covid_A6_k(covid_A6_k, "Switzerland")
logi_icu_cases_united_states <- logi_icu_cases_covid_A6_k(covid_A6_k, "United States")
logi_deaths_icu_chile <- logi_deaths_icu_covid_A6_k(covid_A6_k, "Chile")
logi_deaths_icu_malaysia <- logi_deaths_icu_covid_A6_k(covid_A6_k, "Malaysia")
logi_deaths_icu_switzerland <- logi_deaths_icu_covid_A6_k(covid_A6_k, "Switzerland")
logi_deaths_icu_united_states <- logi_deaths_icu_covid_A6_k(covid_A6_k, "United States")
logi_icu_deaths_chile <- logi_icu_deaths_covid_A6_k(covid_A6_k, "Chile")
logi_icu_deaths_malaysia <- logi_icu_deaths_covid_A6_k(covid_A6_k, "Malaysia")
logi_icu_deaths_switzerland <- logi_icu_deaths_covid_A6_k(covid_A6_k, "Switzerland")
logi_icu_deaths_united_states <- logi_icu_deaths_covid_A6_k(covid_A6_k, "United States")print(logi_cases_deaths_chile)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -193.77 -46.15 -12.18 55.64 177.23
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 24.294 5.844 4.157
## covid_location$new_deaths_smoothed_per_million 38.319 1.499 25.567
## Pr(>|t|)
## (Intercept) 3.71e-05 ***
## covid_location$new_deaths_smoothed_per_million < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5105.523)
##
## Null deviance: 6273094 on 576 degrees of freedom
## Residual deviance: 2935675 on 575 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 6567.9
##
## Number of Fisher Scoring iterations: 2print(logi_cases_deaths_malaysia)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -271.19 -41.06 -6.11 28.12 213.39
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 43.3992 2.8832 15.05
## covid_location$new_deaths_smoothed_per_million 61.2595 0.9112 67.23
## Pr(>|t|)
## (Intercept) <2e-16 ***
## covid_location$new_deaths_smoothed_per_million <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3506.804)
##
## Null deviance: 17872626 on 578 degrees of freedom
## Residual deviance: 2023426 on 577 degrees of freedom
## AIC: 6373.2
##
## Number of Fisher Scoring iterations: 2print(logi_cases_deaths_switzerland)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -216.50 -137.64 -78.63 25.02 1461.80
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 150.232 11.991 12.53
## covid_location$new_deaths_smoothed_per_million 44.914 3.091 14.53
## Pr(>|t|)
## (Intercept) <2e-16 ***
## covid_location$new_deaths_smoothed_per_million <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 56847.03)
##
## Null deviance: 43777224 on 560 degrees of freedom
## Residual deviance: 31777490 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 7737.9
##
## Number of Fisher Scoring iterations: 2print(logi_cases_deaths_united_states)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -243.90 -68.44 -26.26 65.71 889.38
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 41.657 9.706 4.292
## covid_location$new_deaths_smoothed_per_million 61.001 2.219 27.491
## Pr(>|t|)
## (Intercept) 2.08e-05 ***
## covid_location$new_deaths_smoothed_per_million < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 15336.43)
##
## Null deviance: 20439736 on 578 degrees of freedom
## Residual deviance: 8849122 on 577 degrees of freedom
## AIC: 7227.5
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_cases_chile)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7592 -0.8353 -0.1539 0.3604 6.9866
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.233398 0.100502 12.27
## covid_location$new_cases_smoothed_per_million 0.013884 0.000543 25.57
## Pr(>|t|)
## (Intercept) <2e-16 ***
## covid_location$new_cases_smoothed_per_million <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.849875)
##
## Null deviance: 2272.9 on 576 degrees of freedom
## Residual deviance: 1063.7 on 575 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 1996.4
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_cases_malaysia)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5474 -0.4597 -0.0348 0.4148 5.1780
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.4416168 0.0489668 -9.019
## covid_location$new_cases_smoothed_per_million 0.0144759 0.0002153 67.228
## Pr(>|t|)
## (Intercept) <2e-16 ***
## covid_location$new_cases_smoothed_per_million <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.8286738)
##
## Null deviance: 4223.38 on 578 degrees of freedom
## Residual deviance: 478.14 on 577 degrees of freedom
## AIC: 1538.3
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_cases_switzerland)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.6310 -1.0226 -0.6781 -0.4790 7.5161
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.6132476 0.1560530 3.93
## covid_location$new_cases_smoothed_per_million 0.0061030 0.0004201 14.53
## Pr(>|t|)
## (Intercept) 9.57e-05 ***
## covid_location$new_cases_smoothed_per_million < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7.72451)
##
## Null deviance: 5948.6 on 560 degrees of freedom
## Residual deviance: 4318.0 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 2743
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_cases_united_states)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.1554 -0.7286 -0.2030 0.5309 4.5767
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.2184078 0.1106459 11.01
## covid_location$new_cases_smoothed_per_million 0.0092960 0.0003381 27.49
## Pr(>|t|)
## (Intercept) <2e-16 ***
## covid_location$new_cases_smoothed_per_million <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2.337124)
##
## Null deviance: 3114.8 on 578 degrees of freedom
## Residual deviance: 1348.5 on 577 degrees of freedom
## AIC: 2138.7
##
## Number of Fisher Scoring iterations: 2print(logi_cases_icu_chile)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -136.59 -77.43 -46.91 64.58 231.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 152.691 4.335 35.22 <2e-16
## covid_location$new_icu_covid_per_million 3.771 4.143 0.91 0.363
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 10879.66)
##
## Null deviance: 6286579 on 578 degrees of freedom
## Residual deviance: 6277563 on 577 degrees of freedom
## AIC: 7028.7
##
## Number of Fisher Scoring iterations: 2print(logi_cases_icu_malaysia)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -184.23 -120.59 -60.99 35.89 566.09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 144.813 7.268 19.926 < 2e-16
## covid_location$new_icu_covid_per_million -31.636 11.415 -2.771 0.00576
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 30568.17)
##
## Null deviance: 17872626 on 578 degrees of freedom
## Residual deviance: 17637832 on 577 degrees of freedom
## AIC: 7626.9
##
## Number of Fisher Scoring iterations: 2print(logi_cases_icu_switzerland)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -411.48 -193.82 -87.50 84.28 1530.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 244.837 11.502 21.286 < 2e-16
## covid_location$new_icu_covid_per_million 9.212 2.119 4.348 1.63e-05
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 76474.7)
##
## Null deviance: 45571463 on 578 degrees of freedom
## Residual deviance: 44125905 on 577 degrees of freedom
## AIC: 8157.8
##
## Number of Fisher Scoring iterations: 2print(logi_cases_icu_united_states)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -340.22 -137.89 -60.49 109.05 899.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 266.960 7.816 34.157 <2e-16
## covid_location$new_icu_covid_per_million 9.803 5.342 1.835 0.067
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 35218.59)
##
## Null deviance: 20439736 on 578 degrees of freedom
## Residual deviance: 20321124 on 577 degrees of freedom
## AIC: 7708.9
##
## Number of Fisher Scoring iterations: 2print(logi_icu_cases_chile)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.8373 -0.6901 -0.0150 0.6318 4.3695
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.0648008 0.0772183 -0.839
## covid_location$new_cases_smoothed_per_million 0.0003803 0.0004178 0.910
## Pr(>|t|)
## (Intercept) 0.402
## covid_location$new_cases_smoothed_per_million 0.363
##
## (Dispersion parameter for gaussian family taken to be 1.097185)
##
## Null deviance: 633.99 on 578 degrees of freedom
## Residual deviance: 633.08 on 577 degrees of freedom
## AIC: 1700.8
##
## Number of Fisher Scoring iterations: 2print(logi_icu_cases_malaysia)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.9241 -0.2134 -0.0559 0.1604 3.6334
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.0736038 0.0340736 2.160
## covid_location$new_cases_smoothed_per_million -0.0004153 0.0001498 -2.771
## Pr(>|t|)
## (Intercept) 0.03117 *
## covid_location$new_cases_smoothed_per_million 0.00576 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.4012497)
##
## Null deviance: 234.60 on 578 degrees of freedom
## Residual deviance: 231.52 on 577 degrees of freedom
## AIC: 1118.4
##
## Number of Fisher Scoring iterations: 2print(logi_icu_deaths_switzerland)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -47.532 -2.565 -0.271 2.051 38.519
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.27159 0.27687 0.981
## covid_location$new_deaths_smoothed_per_million -0.01086 0.07138 -0.152
## Pr(>|t|)
## (Intercept) 0.327
## covid_location$new_deaths_smoothed_per_million 0.879
##
## (Dispersion parameter for gaussian family taken to be 30.30636)
##
## Null deviance: 16942 on 560 degrees of freedom
## Residual deviance: 16941 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 3509.8
##
## Number of Fisher Scoring iterations: 2print(logi_icu_cases_united_states)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -6.0459 -0.5191 -0.0274 0.4329 26.6911
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.0633050 0.1055490 -0.600
## covid_location$new_cases_smoothed_per_million 0.0005920 0.0003226 1.835
## Pr(>|t|)
## (Intercept) 0.549
## covid_location$new_cases_smoothed_per_million 0.067 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2.126764)
##
## Null deviance: 1234.3 on 578 degrees of freedom
## Residual deviance: 1227.1 on 577 degrees of freedom
## AIC: 2084
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_icu_chile)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2725 -1.3852 -0.3746 1.2419 9.1706
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.35749 0.08243 40.730 <2e-16
## covid_location$new_icu_covid_per_million 0.17154 0.07883 2.176 0.0299
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3.92061)
##
## Null deviance: 2272.9 on 576 degrees of freedom
## Residual deviance: 2254.4 on 575 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 2429.8
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_icu_malaysia)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5894 -1.6173 -1.2808 0.4422 11.7333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6581 0.1109 14.952 < 2e-16
## covid_location$new_icu_covid_per_million -0.7064 0.1742 -4.056 5.68e-05
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7.116658)
##
## Null deviance: 4223.4 on 578 degrees of freedom
## Residual deviance: 4106.3 on 577 degrees of freedom
## AIC: 2783.4
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_icu_switzerland)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1272 -1.9521 -1.3896 -0.1477 9.0979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.108861 0.137865 15.297 <2e-16
## covid_location$new_icu_covid_per_million -0.003812 0.025062 -0.152 0.879
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 10.64098)
##
## Null deviance: 5948.6 on 560 degrees of freedom
## Residual deviance: 5948.3 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 2922.6
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_icu_united_states)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2913 -1.6049 -0.7152 1.1588 7.2036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.73951 0.09474 39.470 < 2e-16
## covid_location$new_icu_covid_per_million -0.32297 0.06475 -4.988 8.09e-07
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.175157)
##
## Null deviance: 3114.8 on 578 degrees of freedom
## Residual deviance: 2986.1 on 577 degrees of freedom
## AIC: 2598.9
##
## Number of Fisher Scoring iterations: 2print(logi_icu_deaths_chile)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.7679 -0.6872 -0.0283 0.6470 4.2020
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.16651 0.08533 -1.951
## covid_location$new_deaths_smoothed_per_million 0.04762 0.02188 2.176
## Pr(>|t|)
## (Intercept) 0.0515 .
## covid_location$new_deaths_smoothed_per_million 0.0299 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.088412)
##
## Null deviance: 630.99 on 576 degrees of freedom
## Residual deviance: 625.84 on 575 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 1690.3
##
## Number of Fisher Scoring iterations: 2print(logi_icu_deaths_malaysia)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.9257 -0.2229 -0.0698 0.1500 3.6203
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.078333 0.030612 2.559
## covid_location$new_deaths_smoothed_per_million -0.039240 0.009675 -4.056
## Pr(>|t|)
## (Intercept) 0.0108 *
## covid_location$new_deaths_smoothed_per_million 5.68e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.3953205)
##
## Null deviance: 234.6 on 578 degrees of freedom
## Residual deviance: 228.1 on 577 degrees of freedom
## AIC: 1109.8
##
## Number of Fisher Scoring iterations: 2print(logi_icu_deaths_switzerland)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -47.532 -2.565 -0.271 2.051 38.519
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.27159 0.27687 0.981
## covid_location$new_deaths_smoothed_per_million -0.01086 0.07138 -0.152
## Pr(>|t|)
## (Intercept) 0.327
## covid_location$new_deaths_smoothed_per_million 0.879
##
## (Dispersion parameter for gaussian family taken to be 30.30636)
##
## Null deviance: 16942 on 560 degrees of freedom
## Residual deviance: 16941 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 3509.8
##
## Number of Fisher Scoring iterations: 2print(logi_icu_deaths_united_states)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -6.2035 -0.6156 -0.2304 0.4187 26.4649
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.56994 0.11224 5.078
## covid_location$new_deaths_smoothed_per_million -0.12798 0.02566 -4.988
## Pr(>|t|)
## (Intercept) 5.16e-07 ***
## covid_location$new_deaths_smoothed_per_million 8.09e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2.050754)
##
## Null deviance: 1234.3 on 578 degrees of freedom
## Residual deviance: 1183.3 on 577 degrees of freedom
## AIC: 2063
##
## Number of Fisher Scoring iterations: 23.1.2 By Develop
logi_cases_deaths_developed <- logi_cases_deaths_covid_A6_k(covid_A6_k_develop, "Yes")
logi_cases_deaths_developing <- logi_cases_deaths_covid_A6_k(covid_A6_k_develop, "No")
logi_deaths_cases_developed <- logi_deaths_cases_covid_A6_k(covid_A6_k_develop, "Yes")
logi_deaths_cases_developing <- logi_deaths_cases_covid_A6_k(covid_A6_k_develop, "No")
logi_cases_icu_developed <- logi_cases_icu_covid_A6_k(covid_A6_k_develop, "Yes")
logi_cases_icu_developing <- logi_cases_icu_covid_A6_k(covid_A6_k_develop, "No")
logi_icu_cases_developed <- logi_icu_cases_covid_A6_k(covid_A6_k_develop, "Yes")
logi_icu_cases_developing <- logi_icu_cases_covid_A6_k(covid_A6_k_develop, "No")
logi_deaths_icu_developed <- logi_deaths_icu_covid_A6_k(covid_A6_k_develop, "Yes")
logi_deaths_icu_developing <- logi_deaths_icu_covid_A6_k(covid_A6_k_develop, "No")
logi_icu_deaths_developed <- logi_icu_deaths_covid_A6_k(covid_A6_k_develop, "Yes")
logi_icu_deaths_developing <- logi_icu_deaths_covid_A6_k(covid_A6_k_develop, "No")print(logi_cases_deaths_developed)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -217.49 -87.13 -52.47 29.49 1173.52
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 92.475 9.976 9.269
## covid_location$new_deaths_smoothed_per_million 56.481 2.601 21.711
## Pr(>|t|)
## (Intercept) <2e-16 ***
## covid_location$new_deaths_smoothed_per_million <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 24172.01)
##
## Null deviance: 24906566 on 560 degrees of freedom
## Residual deviance: 13512153 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 7258.2
##
## Number of Fisher Scoring iterations: 2print(logi_cases_deaths_developing)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -177.903 -39.377 6.408 40.131 110.379
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 25.986 4.095 6.346
## covid_location$new_deaths_smoothed_per_million 49.015 1.421 34.503
## Pr(>|t|)
## (Intercept) 4.47e-10 ***
## covid_location$new_deaths_smoothed_per_million < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2381.837)
##
## Null deviance: 4205087 on 576 degrees of freedom
## Residual deviance: 1369557 on 575 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 6128
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_cases_developed)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -9.3153 -0.8688 -0.3255 0.4353 4.3187
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.8176594 0.1235736 6.617
## covid_location$new_cases_smoothed_per_million 0.0080998 0.0003731 21.711
## Pr(>|t|)
## (Intercept) 8.61e-11 ***
## covid_location$new_cases_smoothed_per_million < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3.46643)
##
## Null deviance: 3571.8 on 560 degrees of freedom
## Residual deviance: 1937.7 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 2293.4
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_cases_developing)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3424 -0.5663 -0.1344 0.3595 3.6768
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.4575358 0.0683465 6.694
## covid_location$new_cases_smoothed_per_million 0.0137572 0.0003987 34.503
## Pr(>|t|)
## (Intercept) 5.16e-11 ***
## covid_location$new_cases_smoothed_per_million < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.6685151)
##
## Null deviance: 1180.3 on 576 degrees of freedom
## Residual deviance: 384.4 on 575 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 1409.1
##
## Number of Fisher Scoring iterations: 2print(logi_cases_icu_developed)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -291.45 -164.23 -65.93 123.75 1215.50
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.910 8.711 29.378 < 2e-16
## covid_location$new_icu_covid_per_million 9.320 2.975 3.133 0.00182
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 43806.75)
##
## Null deviance: 25706568 on 578 degrees of freedom
## Residual deviance: 25276494 on 577 degrees of freedom
## AIC: 7835.2
##
## Number of Fisher Scoring iterations: 2print(logi_cases_icu_developing)##
## Call:
## glm(formula = covid_location$new_cases_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -108.271 -86.596 -6.939 53.796 199.326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 148.520 3.551 41.826 <2e-16
## covid_location$new_icu_covid_per_million 1.118 5.866 0.191 0.849
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7300.177)
##
## Null deviance: 4212467 on 578 degrees of freedom
## Residual deviance: 4212202 on 577 degrees of freedom
## AIC: 6797.7
##
## Number of Fisher Scoring iterations: 2print(logi_icu_cases_developed)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -23.4436 -1.4081 -0.0623 1.2326 19.3494
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.3038025 0.1905650 -1.594
## covid_location$new_cases_smoothed_per_million 0.0017951 0.0005729 3.133
## Pr(>|t|)
## (Intercept) 0.11143
## covid_location$new_cases_smoothed_per_million 0.00182 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 8.437216)
##
## Null deviance: 4951.1 on 578 degrees of freedom
## Residual deviance: 4868.3 on 577 degrees of freedom
## AIC: 2881.9
##
## Number of Fisher Scoring iterations: 2print(logi_icu_cases_developing)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_cases_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.90104 -0.36066 -0.00858 0.34799 2.60271
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -4.913e-03 5.060e-02 -0.097
## covid_location$new_cases_smoothed_per_million 5.633e-05 2.954e-04 0.191
## Pr(>|t|)
## (Intercept) 0.923
## covid_location$new_cases_smoothed_per_million 0.849
##
## (Dispersion parameter for gaussian family taken to be 0.3677162)
##
## Null deviance: 212.19 on 578 degrees of freedom
## Residual deviance: 212.17 on 577 degrees of freedom
## AIC: 1067.9
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_icu_developed)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8409 -1.7198 -0.9238 0.6070 6.8797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.89950 0.10661 27.198 <2e-16
## covid_location$new_icu_covid_per_million -0.06492 0.03594 -1.806 0.0714
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6.352497)
##
## Null deviance: 3571.8 on 560 degrees of freedom
## Residual deviance: 3551.0 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 2633.2
##
## Number of Fisher Scoring iterations: 2print(logi_deaths_icu_developing)##
## Call:
## glm(formula = covid_location$new_deaths_smoothed_per_million ~
## covid_location$new_icu_covid_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6140 -1.2011 -0.4383 1.2776 4.3514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.50233 0.05964 41.957 <2e-16
## covid_location$new_icu_covid_per_million 0.02981 0.09879 0.302 0.763
##
## (Intercept) ***
## covid_location$new_icu_covid_per_million
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2.052284)
##
## Null deviance: 1180.3 on 576 degrees of freedom
## Residual deviance: 1180.1 on 575 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 2056.3
##
## Number of Fisher Scoring iterations: 2print(logi_icu_deaths_developed)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -23.7315 -1.5620 -0.3318 1.2816 19.0458
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.43788 0.18976 2.308
## covid_location$new_deaths_smoothed_per_million -0.08938 0.04948 -1.806
## Pr(>|t|)
## (Intercept) 0.0214 *
## covid_location$new_deaths_smoothed_per_million 0.0714 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 8.745178)
##
## Null deviance: 4917.1 on 560 degrees of freedom
## Residual deviance: 4888.6 on 559 degrees of freedom
## (18 observations deleted due to missingness)
## AIC: 2812.6
##
## Number of Fisher Scoring iterations: 2print(logi_icu_deaths_developing)##
## Call:
## glm(formula = covid_location$new_icu_covid_per_million ~ covid_location$new_deaths_smoothed_per_million)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.90621 -0.35909 -0.00876 0.34728 2.59785
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.008725 0.050735 -0.172
## covid_location$new_deaths_smoothed_per_million 0.005312 0.017602 0.302
## Pr(>|t|)
## (Intercept) 0.864
## covid_location$new_deaths_smoothed_per_million 0.763
##
## (Dispersion parameter for gaussian family taken to be 0.3656768)
##
## Null deviance: 210.30 on 576 degrees of freedom
## Residual deviance: 210.26 on 575 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 1061
##
## Number of Fisher Scoring iterations: 23.2 Correlation
3.2.1 By Countries
new_c_d_i_chile <- covid_location_covid_A6_k(covid_A6_k, "Chile")
new_c_d_i_malaysia <- covid_location_covid_A6_k(covid_A6_k, "Malaysia")
new_c_d_i_switzerland <- covid_location_covid_A6_k(covid_A6_k, "Switzerland")
new_c_d_i_united_states <- covid_location_covid_A6_k(covid_A6_k, "United States")
cor_cases_deaths_icu_chile <- Hmisc::rcorr(as.matrix(new_c_d_i_chile[, 3:5]))
cor_cases_deaths_icu_chile$r[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.03786974 0.09038519
## new_icu_covid_per_million
## 1.00000000cor_cases_deaths_icu_chile$P[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.36303638 0.02994149
## new_icu_covid_per_million
## NAggpairs(new_c_d_i_chile, columns = 3:5)
cor_cases_deaths_icu_malaysia <- Hmisc::rcorr(as.matrix(new_c_d_i_malaysia[, 3:5]))
cor_cases_deaths_icu_malaysia$r[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## -0.1146171 -0.1664924
## new_icu_covid_per_million
## 1.0000000cor_cases_deaths_icu_malaysia$P[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.0057605419 0.0000568102
## new_icu_covid_per_million
## NAggpairs(new_c_d_i_malaysia, columns = 3:5)
cor_cases_deaths_icu_switzerland <- Hmisc::rcorr(as.matrix(new_c_d_i_switzerland[, 3:5]))
cor_cases_deaths_icu_switzerland$r[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.178103023 -0.006433335
## new_icu_covid_per_million
## 1.000000000cor_cases_deaths_icu_switzerland$P[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 1.626351e-05 8.791569e-01
## new_icu_covid_per_million
## NAggpairs(new_c_d_i_switzerland, columns = 3:5)
cor_cases_deaths_united_states <- Hmisc::rcorr(as.matrix(new_c_d_i_united_states[, 3:5]))
cor_cases_deaths_united_states$r[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.07617732 -0.20331089
## new_icu_covid_per_million
## 1.00000000cor_cases_deaths_united_states$P[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 6.699440e-02 8.089554e-07
## new_icu_covid_per_million
## NAggpairs(new_c_d_i_united_states, columns = 3:5)
3.2.2 By Develop
new_c_d_i_developed <- covid_location_covid_A6_k(covid_A6_k_develop, "Yes")
new_c_d_i_developing <- covid_location_covid_A6_k(covid_A6_k_develop, "No")
cor_cases_deaths_icu_developed <- Hmisc::rcorr(as.matrix(new_c_d_i_developed[, 3:5]))
cor_cases_deaths_icu_developed$r[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.12934489 -0.07617391
## new_icu_covid_per_million
## 1.00000000cor_cases_deaths_icu_developed$P[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.00181629 0.07141835
## new_icu_covid_per_million
## NAggpairs(new_c_d_i_developed, columns = 3:5)
cor_cases_deaths_icu_developing <- Hmisc::rcorr(as.matrix(new_c_d_i_developing[, 3:5]))
cor_cases_deaths_icu_developing$r[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.007936416 0.012584026
## new_icu_covid_per_million
## 1.000000000cor_cases_deaths_icu_developing$P[3,]## new_cases_smoothed_per_million new_deaths_smoothed_per_million
## 0.8488706 0.7629302
## new_icu_covid_per_million
## NAggpairs(new_c_d_i_developing, columns = 3:5)
4 Model
4.1 ARIMA
4.1.1 By Countries
developed_countries <- filter(covid_A6_k, developed == "Yes")
developing_countries <- filter(covid_A6_k, developed == "No")
tsdisplay(developed_countries$new_icu_covid_per_million)
tsdisplay(developing_countries$new_icu_covid_per_million)
fit <- auto.arima(developed_countries$new_icu_covid_per_million)
forecast(fit, 30)## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 1159 0.14047300 -4.440499 4.721445 -6.865519 7.146465
## 1160 0.57763100 -4.066359 5.221621 -6.524739 7.680001
## 1161 0.76916257 -3.885280 5.423605 -6.349193 7.887518
## 1162 0.70977451 -3.962670 5.382219 -6.436113 7.855662
## 1163 0.81256364 -3.873955 5.499082 -6.354848 7.979975
## 1164 0.95575655 -3.733517 5.645030 -6.215869 8.127382
## 1165 0.83252849 -3.894496 5.559553 -6.396832 8.061889
## 1166 0.71924759 -4.072683 5.511179 -6.609379 8.047874
## 1167 0.81345636 -3.993520 5.620433 -6.538180 8.165093
## 1168 0.78114509 -4.042539 5.604829 -6.596044 8.158334
## 1169 0.58702108 -4.297297 5.471340 -6.882899 8.056942
## 1170 0.57513805 -4.339585 5.489861 -6.941283 8.091559
## 1171 0.63826258 -4.281608 5.558134 -6.886031 8.162556
## 1172 0.49615405 -4.448950 5.441258 -7.066730 8.059038
## 1173 0.37434829 -4.608000 5.356697 -7.245496 7.994193
## 1174 0.44723182 -4.539746 5.434210 -7.179692 8.074156
## 1175 0.42484870 -4.565955 5.415653 -7.207928 8.057625
## 1176 0.26430201 -4.751605 5.280209 -7.406865 7.935469
## 1177 0.26240249 -4.763274 5.288079 -7.423706 7.948511
## 1178 0.33333636 -4.692423 5.359096 -7.352899 8.019572
## 1179 0.22860462 -4.804135 5.261344 -7.468306 7.925515
## 1180 0.13640104 -4.910388 5.183190 -7.581996 7.854798
## 1181 0.21432760 -4.832678 5.261333 -7.504402 7.933057
## 1182 0.21386748 -4.833230 5.260965 -7.505003 7.932738
## 1183 0.08946476 -4.967040 5.145970 -7.643792 7.822722
## 1184 0.09862338 -4.960548 5.157795 -7.638712 7.835959
## 1185 0.17450060 -4.885266 5.234267 -7.563744 7.912745
## 1186 0.09803740 -4.963429 5.159504 -7.642807 7.838882
## 1187 0.02659964 -5.040777 5.093976 -7.723283 7.776483
## 1188 0.10277680 -4.964691 5.170244 -7.647246 7.852799plot(forecast(fit, 30))
fit <- auto.arima(developing_countries$new_icu_covid_per_million)
forecast(fit, 30)## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 1159 1.336403e-02 -1.097017 1.123745 -1.684816 1.711545
## 1160 8.139009e-03 -1.102536 1.118814 -1.690492 1.706770
## 1161 -4.958801e-04 -1.111453 1.110461 -1.699558 1.698567
## 1162 -1.763532e-04 -1.111134 1.110782 -1.699240 1.698887
## 1163 1.550453e-05 -1.110943 1.110974 -1.699048 1.699079
## 1164 3.711476e-06 -1.110954 1.110962 -1.699060 1.699067
## 1165 -4.432394e-07 -1.110958 1.110958 -1.699064 1.699063
## 1166 -7.541596e-08 -1.110958 1.110958 -1.699064 1.699064
## 1167 1.196408e-08 -1.110958 1.110958 -1.699064 1.699064
## 1168 1.464274e-09 -1.110958 1.110958 -1.699064 1.699064
## 1169 -3.097725e-10 -1.110958 1.110958 -1.699064 1.699064
## 1170 -2.664494e-11 -1.110958 1.110958 -1.699064 1.699064
## 1171 7.760938e-12 -1.110958 1.110958 -1.699064 1.699064
## 1172 4.359032e-13 -1.110958 1.110958 -1.699064 1.699064
## 1173 -1.891020e-13 -1.110958 1.110958 -1.699064 1.699064
## 1174 -5.699434e-15 -1.110958 1.110958 -1.699064 1.699064
## 1175 4.494221e-15 -1.110958 1.110958 -1.699064 1.699064
## 1176 2.793297e-17 -1.110958 1.110958 -1.699064 1.699064
## 1177 -1.043326e-16 -1.110958 1.110958 -1.699064 1.699064
## 1178 1.759719e-18 -1.110958 1.110958 -1.699064 1.699064
## 1179 2.366570e-18 -1.110958 1.110958 -1.699064 1.699064
## 1180 -9.513335e-20 -1.110958 1.110958 -1.699064 1.699064
## 1181 -5.240837e-20 -1.110958 1.110958 -1.699064 1.699064
## 1182 3.402546e-21 -1.110958 1.110958 -1.699064 1.699064
## 1183 1.130739e-21 -1.110958 1.110958 -1.699064 1.699064
## 1184 -1.045583e-22 -1.110958 1.110958 -1.699064 1.699064
## 1185 -2.367859e-23 -1.110958 1.110958 -1.699064 1.699064
## 1186 2.957963e-24 -1.110958 1.110958 -1.699064 1.699064
## 1187 4.781415e-25 -1.110958 1.110958 -1.699064 1.699064
## 1188 -7.926295e-26 -1.110958 1.110958 -1.699064 1.699064plot(forecast(fit, 30))
4.1.2 By Develop
developed_countries <- filter(covid_A6_k_develop, developed == "Yes")
developing_countries <- filter(covid_A6_k_develop, developed == "No")
tsdisplay(developed_countries$new_icu_covid_per_million)
tsdisplay(developing_countries$new_icu_covid_per_million)
fit <- auto.arima(developed_countries$new_icu_covid_per_million)
forecast(fit, 30)## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 580 0.73633132 -2.575697 4.048359 -4.328978 5.801641
## 581 1.14350044 -2.211442 4.498443 -3.987441 6.274442
## 582 0.13139120 -3.231080 3.493862 -5.011065 5.273847
## 583 -0.49772334 -3.871756 2.876309 -5.657861 4.662414
## 584 0.27453723 -3.110326 3.659401 -4.902165 5.451240
## 585 0.21535614 -3.172176 3.602888 -4.965427 5.396139
## 586 -0.73129492 -4.145991 2.683401 -5.953622 4.491032
## 587 -0.41273165 -3.875571 3.050107 -5.708687 4.883224
## 588 0.27259965 -3.203772 3.748972 -5.044052 5.589252
## 589 -0.38196708 -3.872756 3.108822 -5.720668 4.956734
## 590 -0.78187648 -4.319785 2.756032 -6.192641 4.628888
## 591 0.02302542 -3.541528 3.587579 -5.428489 5.474540
## 592 0.07488055 -3.496027 3.645788 -5.386352 5.536113
## 593 -0.70185800 -4.295352 2.891636 -6.197633 4.793917
## 594 -0.38659037 -4.012915 3.239734 -5.932575 5.159394
## 595 0.26026106 -3.372518 3.893040 -5.295595 5.816118
## 596 -0.28946607 -3.927741 3.348809 -5.853728 5.274796
## 597 -0.64131737 -4.303398 3.020764 -6.241987 4.959352
## 598 0.07678773 -3.596676 3.750252 -5.541291 5.694866
## 599 0.13987280 -3.534612 3.814358 -5.479767 5.759513
## 600 -0.54863249 -4.231748 3.134483 -6.181471 5.084206
## 601 -0.28081339 -3.978927 3.417300 -5.936590 5.374963
## 602 0.29758632 -3.401812 3.996985 -5.360155 5.955328
## 603 -0.18401782 -3.884355 3.516320 -5.843196 5.475160
## 604 -0.50886148 -4.219886 3.202163 -6.184384 5.166661
## 605 0.12070516 -3.594520 3.835930 -5.561241 5.802652
## 606 0.18422298 -3.531002 3.899448 -5.497724 5.866169
## 607 -0.43098543 -4.149227 3.287256 -6.117545 5.255575
## 608 -0.20727669 -3.932595 3.518042 -5.904660 5.490106
## 609 0.30770889 -3.417705 4.033123 -5.389821 6.005238plot(forecast(fit, 30))
fit <- auto.arima(developing_countries$new_icu_covid_per_million)
forecast(fit, 30)## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 580 0.04540737 -0.7134836 0.8042984 -1.115216 1.206031
## 581 0.03760924 -0.7219829 0.7972014 -1.124087 1.199305
## 582 0.02905416 -0.7305954 0.7887037 -1.132730 1.190838
## 583 -0.07480923 -0.8383770 0.6887585 -1.242585 1.092967
## 584 -0.10698216 -0.8707917 0.6568273 -1.275128 1.061164
## 585 -0.11099581 -0.8764687 0.6544771 -1.281686 1.059694
## 586 -0.09499544 -0.8633147 0.6733238 -1.270038 1.080047
## 587 -0.08472684 -0.8550134 0.6855598 -1.262779 1.093325
## 588 -0.08157456 -0.8532290 0.6900798 -1.261718 1.098569
## 589 -0.08347907 -0.8562300 0.6892718 -1.265300 1.098341
## 590 -0.08580796 -0.8596844 0.6880685 -1.269350 1.097734
## 591 -0.08694756 -0.8620467 0.6881516 -1.272359 1.098464
## 592 -0.08691453 -0.8633073 0.6894782 -1.274305 1.100476
## 593 -0.08649499 -0.8641930 0.6912030 -1.275881 1.102891
## 594 -0.08618783 -0.8651741 0.6927984 -1.277544 1.105169
## 595 -0.08611376 -0.8663696 0.6941420 -1.279412 1.107185
## 596 -0.08616838 -0.8676850 0.6953483 -1.281395 1.109058
## 597 -0.08623627 -0.8690126 0.6965400 -1.283389 1.110917
## 598 -0.08626684 -0.8703039 0.6977702 -1.285348 1.112814
## 599 -0.08626515 -0.8715628 0.6990325 -1.287274 1.114744
## 600 -0.08625282 -0.8728095 0.7003039 -1.289187 1.116682
## 601 -0.08624426 -0.8740575 0.7015690 -1.291101 1.118612
## 602 -0.08624235 -0.8753097 0.7028250 -1.293017 1.120532
## 603 -0.08624402 -0.8765633 0.7040752 -1.294933 1.122445
## 604 -0.08624595 -0.8778151 0.7053232 -1.296847 1.124355
## 605 -0.08624678 -0.8790641 0.7065705 -1.298756 1.126263
## 606 -0.08624671 -0.8803102 0.7078167 -1.300662 1.128169
## 607 -0.08624635 -0.8815540 0.7090613 -1.302564 1.130072
## 608 -0.08624611 -0.8827961 0.7103038 -1.304464 1.131972
## 609 -0.08624606 -0.8840363 0.7115442 -1.306361 1.133869plot(forecast(fit, 30))